how is the affect of ordering the features in perceptron?

Question

I Have 2 linearly separable classes and I have performed a simple perceptron for finding the classifier's threshold.

Since in simple perceptron I used all missclassified points in every iteration of algorithm I was wondering if :

1. does the order of accounting points affect the out put threshold line?
2. does the order of accounting points affect the number of iterations needed to achieve the line?

Answer 1

Yes to both questions. You should experiment with a small example if you want to see this happening. Here is an example of a perceptron in R:

fit <- function(dat, y, w){

# dat a n x 2 matrix of data points
# y an n-vector of 0/1 classifications
# w starting weight vector

counter <- 0 # count number of iterations
error <- 100
n <- nrow(dat)

while (error > 0){
  error <- 0
  for (i in 1:n){
    fitted <- sum(w*dat[i,]) > 0
    if (fitted != y[i]){
      error <- error + 1
    }
    w <- w + (y[i] - fitted)*dat[i,]
  }
  counter <- counter + 1
}
print(w) # final value of w
print(counter) # number of iterations through data set until convergence
}

You can try creating a small data set to classify:

dat <- matrix(c(1,1,2,2,2,1,1,0), nc=2)
y <- c(1, 1, 0, 0) # true classes
w <- c(0, 0) # weights

Classify it:

> fit(dat, y, w)
[1] -2  3
[1] 5
> z <- c(3, 2, 1, 4) # re-order data
> fit(dat[z, ], y[z], w)
[1] -1  2
[1] 4

You can see that it reaches a different solution and takes a different number of iterations to do so.